Add SAM 3D Body MCP server implementation

- Gradio app with GPU support for body mesh reconstruction
- Image to 3D GLB export via Blender
- MCP server integration for AI agents

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

README.md

@@ -1,12 +1,41 @@
 ---
-title: Sam3d Body Mcp
-emoji: 👀
-colorFrom: yellow
-colorTo: blue
+title: SAM 3D Body MCP
+emoji: 🧍
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 6.0.2
+sdk_version: 5.9.1
 app_file: app.py
 pinned: false
+license: other
+hardware: zero-a10g
+tags:
+  - mcp-server-track
+  - building-mcp-track-consumer
+  - agents
+  - 3d
+  - mesh-recovery
+  - sam3d
+short_description: "Image → 3D Human Mesh (GLB) - MCP Server"
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🧍 SAM 3D Body MCP Server
+
+**Image → 3D Human Mesh** powered by [Meta's SAM 3D Body](https://github.com/facebookresearch/sam-3d-body)
+
+## MCP Integration
+
+```json
+{
+  "mcpServers": {
+    "sam3d": {
+      "command": "npx",
+      "args": ["mcp-remote", "https://dev-bjoern-sam3d-body-mcp.hf.space/gradio_api/mcp/sse"]
+    }
+  }
+}
+```
+
+## Credits
+- [facebook/sam-3d-body-dinov3](https://huggingface.co/facebook/sam-3d-body-dinov3)
+- [rerun-io/sam3d-body-rerun](https://github.com/rerun-io/sam3d-body-rerun)

app.py

@@ -0,0 +1,167 @@
+"""
+SAM 3D Body MCP Server
+Image → 3D Human Mesh (GLB)
+"""
+import os
+import sys
+import subprocess
+import tempfile
+import uuid
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+import spaces
+import torch
+import bpy
+from huggingface_hub import snapshot_download
+from PIL import Image
+
+# Clone sam-3d-body repo if not exists
+SAM3D_PATH = Path("/home/user/app/sam-3d-body")
+if not SAM3D_PATH.exists():
+    print("Cloning sam-3d-body repository...")
+    subprocess.run([
+        "git", "clone", 
+        "https://github.com/facebookresearch/sam-3d-body.git",
+        str(SAM3D_PATH)
+    ], check=True)
+    sys.path.insert(0, str(SAM3D_PATH))
+
+# Add to path
+sys.path.insert(0, str(SAM3D_PATH))
+
+# Global model
+MODEL = None
+FACES = None
+
+def load_model():
+    """Load SAM 3D Body model"""
+    global MODEL, FACES
+    
+    if MODEL is not None:
+        return MODEL, FACES
+    
+    print("Loading SAM 3D Body model...")
+    
+    # Download checkpoint
+    checkpoint_dir = snapshot_download(
+        repo_id="facebook/sam-3d-body-dinov3",
+        token=os.environ.get("HF_TOKEN")
+    )
+    
+    from sam_3d_body import load_sam_3d_body, SAM3DBodyEstimator
+    
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    model, model_cfg = load_sam_3d_body(
+        checkpoint_path=f"{checkpoint_dir}/model.ckpt",
+        device=device,
+        mhr_path=f"{checkpoint_dir}/assets/mhr_model.pt"
+    )
+    
+    MODEL = SAM3DBodyEstimator(
+        sam_3d_body_model=model,
+        model_cfg=model_cfg,
+    )
+    FACES = MODEL.faces
+    
+    print("✓ Model loaded")
+    return MODEL, FACES
+
+
+@spaces.GPU(duration=60)
+def reconstruct_body(image: np.ndarray) -> tuple:
+    """
+    Reconstruct 3D body mesh from image.
+    
+    Args:
+        image: Input RGB image
+    
+    Returns:
+        tuple: (glb_path, status)
+    """
+    if image is None:
+        return None, "❌ No image provided"
+    
+    try:
+        estimator, faces = load_model()
+        
+        # Process image
+        if isinstance(image, Image.Image):
+            image = np.array(image)
+        
+        # BGR for OpenCV
+        import cv2
+        img_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        
+        outputs = estimator.process_one_image(img_bgr, bbox_thr=0.5)
+        
+        if not outputs:
+            return None, "⚠️ No humans detected"
+        
+        # Export first person as GLB via Blender
+        person = outputs[0]
+        vertices = person["pred_vertices"]
+        
+        # Reset Blender scene
+        bpy.ops.wm.read_factory_settings(use_empty=True)
+        
+        # Create mesh
+        mesh = bpy.data.meshes.new("body_mesh")
+        mesh.from_pydata(vertices.tolist(), [], faces.tolist())
+        mesh.update()
+        
+        # Create object
+        obj = bpy.data.objects.new("body", mesh)
+        bpy.context.collection.objects.link(obj)
+        bpy.context.view_layer.objects.active = obj
+        obj.select_set(True)
+        
+        # Smooth shading
+        for poly in mesh.polygons:
+            poly.use_smooth = True
+        
+        # Save GLB
+        output_dir = tempfile.mkdtemp()
+        glb_path = f"{output_dir}/body_{uuid.uuid4().hex[:8]}.glb"
+        bpy.ops.export_scene.gltf(
+            filepath=glb_path,
+            export_format='GLB',
+            use_selection=True
+        )
+        
+        return glb_path, f"✓ Reconstructed {len(outputs)} person(s)"
+        
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return None, f"❌ Error: {e}"
+
+
+# Gradio Interface
+with gr.Blocks(title="SAM 3D Body MCP") as demo:
+    gr.Markdown("# 🧍 SAM 3D Body MCP Server\n**Image → 3D Human Mesh (GLB)**")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Input Image", type="numpy")
+            btn = gr.Button("🎯 Reconstruct", variant="primary")
+        
+        with gr.Column():
+            output_file = gr.File(label="3D Mesh (GLB)")
+            status = gr.Textbox(label="Status")
+    
+    btn.click(reconstruct_body, inputs=[input_image], outputs=[output_file, status])
+    
+    gr.Markdown("""
+    ---
+    ### MCP Server
+    ```json
+    {"mcpServers": {"sam3d": {"command": "npx", "args": ["mcp-remote", "URL/gradio_api/mcp/sse"]}}}
+    ```
+    """)
+
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

requirements.txt

@@ -0,0 +1,20 @@
+torch>=2.2.0
+torchvision>=0.17.0
+gradio>=5.9.0
+huggingface_hub>=0.26.0
+spaces>=0.30.0
+bpy>=4.2.0
+numpy>=1.26.0
+opencv-python>=4.8.0
+Pillow>=10.0.0
+einops>=0.7.0
+timm>=0.9.0
+pytorch-lightning>=2.0.0
+roma>=1.5.0
+yacs>=0.1.8
+hydra-core>=1.3.0
+pyrootutils>=1.0.4
+networkx==3.2.1
+loguru>=0.7.0
+fvcore>=0.1.5
+rich>=13.0.0